Monitoring 2011–2020 Traffic Patterns in Wuhan (China) with COSMO-SkyMed SAR, Amidst the 7th CISM Military World Games and COVID-19 Outbreak
SAR, COSMO-SkyMed, Wuhan, moving target detection, Traffic patterns, COVID-19
Vehicle detection from satellite imagery can support different applications, such as security and situational awareness. In the civilian domain, it can provide quantitative evidence to investigate urban mobility and traffic patterns in cities. Satellite synthetic aperture radar (SAR) can help in detecting vehicles in (almost) all weather conditions and during the day and night. In this study, the capability of SAR StripMap imaging mode data to monitor traffic is analyzed using the case study of Wuhan, China. In ordinary times, the bridges crossing the Yangtze river are the key infrastructure allowing for urban mobility in Wuhan. More recently, the city has been the first in the world to be put in lockdown due to the outbreak of the Coronavirus Disease of 2019 (COVID-19). Using a very long time series of 294 COSMO-SkyMed StripMap HIMAGE mode scenes collected from 2011 to 2020, we detected vehicles on seven bridges, estimated their speed, and analyzed the traffic pattern over time. Vehicles are detected based on their azimuth shift caused by their across-track motion. Our goal is to monitor the variations in traffic instead of single-car detection. The results from 2011 to 2019 show a general increase in the number of vehicles crossing the bridges, as new infrastructure was built over the years. Variations in detected vehicle numbers were especially found during the two events of the 7th International Military Sports Council (CISM) Military World Games in October 2019, and the COVID-19 lockdown in early 2020. These events were therefore used for internal validation of our assessment of traffic patterns. On the other side, TomTom traffic index data were used for external validation. The results and their comparison with TomTom data prove the effectiveness of our method in detecting traffic patterns, but also demonstrate that mostly large vehicles (e.g., trucks or buses) are detected. Future work should be carried out to improve the detection rate of smaller vehicles.
Francesca Cigna, Deodato Tapete, Timo Balz, Hashir Tanveer